Our new network search space illustrated in Figure REF  can be summarized as follows.
We use a ResNet architecture [1]} pretrained on ImageNet[2]} as an encoder network similarly to [3]}, [4]}, [5]}. ResNet is composed of 4 blocs. We connected each block's output to two layers. The first one is a preprocessing layer that we denote \(S_0\) . This layer is composed of a \(3 \times 3 \)  convolution and output 42 feature maps. This operation allows us to control the depth map that will enter the cells.
The second preprocessing applied on the output of \(S_0\) ,
and denoted \(S_1\) , is composed of a \(3 \times 3 \)  convolution and outputs 42 feature maps. The input of the cells are the output of \(S_0\)  and \(S_1\) , so that the cell learns if it wants to use \(S_0\)  or/and \(S_1\) .
After each block, We use a \(1\times 1\)  convolution on the cells' output to reduce the channel's number to one. This convolution is followed by an upsampling to resize all the feature maps to input size and we concatenate all upsampled results. Finally, we use a \(1\times 1\)  to produce the final edge detection map.
